Scalable collaborative filtering using cluster-based smoothing

By Gui-Rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, Zheng Chen. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005

745 citations recorded by the source corpus.

Subfields: Information Systems, Marketing, Transportation

View Paper: https://www.semanticscholar.org/paper/f8596c061df4bd67f81def52bbd5b9ede6eb9693

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Citations in the corpus

Recommender system application developments: A survey (2015, background): “Xue et al. [39] presented a typical use of clustering in recommender systems.”
Recommender systems with social regularization (2011, background): “Related recommendation techniques have been widely studied in research communities of information retrieval [5, 8, 12, 21, 36, 38, 39, 40], machine learning [28, 30, 31, 33, 41] and data mining [1, 3, 11, 14, 15].”
QoS-Aware Web Service Recommendation via Collaborative Filtering (2014, methodology): “Examples include clustering model [33], latent semantic models [11, 12] and latent factor model [5].”
A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering (2010, background): “Traditional memory based collaborative filtering is to search the whole ratings database and it suffers from poor scalability when more and more users and items are added into the database [21].”
Unifying user-based and item-based collaborative filtering approaches by similarity fusion (2006, methodology): “However, as pointed out in [8, 19], some useful information may be discarded during the reduction.”
Localized matrix factorization for recommendation based on matrix block diagonal forms (2013, background): “BBDF and RBBDF structures are generalizations of Block Diagonal Form (BDF) structure which has no ‘border’.”
Recommender Systems (2016, neutral): “A number of clustering methods are described in [146, 167, 528, 643, 644, 647].”
Data Mining Methods for Recommender Systems (2015, methodology): “[77] present a typical use of clustering in the context of a RS by employing the k-means algorithm as a pre-processing step to help in neighborhood for-”
Personalized Recommendation Combining User Interest and Social Circle (2014, neutral): “It can be viewed as the first generation of RSes [7] with traditional collaborative filtering algorithms [8]– [12], [22]–[32] to predict user interest.”
EigenRank: a ranking-oriented approach to collaborative filtering (2008, methodology): “To alleviate such sparsity problem, different techniques have been proposed to fill in some of the unknown ratings in the matrix such as dimensionality reduction[8] and data-smoothing methods[25, 19].”
Single Document Keyphrase Extraction Using Neighborhood Knowledge (2008, methodology): “It is noteworthy that collaborative techniques have been successfully used in the tasks of information filtering (Xue et al., 2005), document summarization (Wan et al., 2007) and web mining (Wong et al., 2006).”
A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques (2018, methodology): “…a cluster- ng method for item-to-item CF system employed at Amazon.com. hey found that the use of clustering improves the scalability issue n CF. Xue et al. (2005) experiments also showed that the combi- ation of memory based and model based methods improve the ecommendation efficiency by…”
A Survey of Collaborative Filtering Techniques (2009, methodology): “Clustering models have better scalability than typical collaborative filtering methods because they make predictions within much smaller clusters rather than the entire customer base [13, 27, 44, 84].”
Circle-based recommendation in online social networks (2012, background): “predict users’ interests by mining user rating history data [1], [2], [4], [6], [16], [23] and [24].”
Collaborative Web Service QoS Prediction via Neighborhood Integrated Matrix Factorization (2013, methodology): “Examples of model-based approaches include the clustering model [41], aspect models [42], and so on.”